Senior Data Engineer
New
Z
ZipFinancial services
Remote-first opportunity for US-based employees, with the option to work in person from our Manhattan office.Full-TimeSenior
SalaryThe annual base Pay Range for this position is $134,000 - $180,000. This range reflects our US national compensation (USN). Additional premium percentages may apply based on our tiered premium strategy. Subject to those same considerations, the total compensation package for this position may also include other elements, including a bonus and/or equity awards.
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Job Details
- Experience
- 5+ years of experience designing, building, and operating production data pipelines and data platforms at scale.
- Required Skills
- PythonSQLApache AirflowSnowflakeApache KafkaCI/CDData modelingBigQuerydbtDatabricks
Requirements
- Bring 5+ years of experience designing, building, and operating production data pipelines and data platforms at scale.
- Have advanced SQL skills and strong Python skills, with a track record of writing maintainable, tested, production-quality code.
- Have deep experience with cloud data platforms such as Snowflake, Databricks, Microsoft Fabric, BigQuery, Redshift, or similar technologies.
- Understand dimensional modeling, slowly changing dimensions, semantic layers, and data design for analytical and operational consumption.
- Have hands-on experience with dbt or equivalent transformation technologies.
- Have experience with workflow orchestration frameworks such as Airflow or Dagster.
- Have experience designing and operating streaming or event-driven architectures using Kafka, Azure Event Hubs, Spark Structured Streaming, or similar platforms.
- Understand data warehouse, data lake, lakehouse, and reusable data product architecture patterns.
- Have production experience with data quality, testing, observability, metadata, lineage, and monitoring practices.
- Have hands-on experience with a major cloud platform; Azure is preferred, with AWS and GCP also listed.
- Apply Git-based workflows, code review, automated testing, CI/CD, infrastructure automation, monitoring, and operational ownership.
- Understand security, privacy, governance, and access-control considerations for enterprise data systems.
- Be able to independently lead complex technical initiatives, mentor engineers, and communicate with technical and non-technical stakeholders.
Responsibilities
- Design, build, and operate scalable batch and streaming pipelines from transactional systems, event streams, APIs, third-party providers, and other sources.
- Develop and maintain dimensional, semantic, and domain-oriented data models for analytics, reporting, finance, risk, product, and operational use cases.
- Build and maintain ELT/ETL workflows with testing, version control, deployment, and monitoring practices.
- Own data quality through validation, freshness checks, anomaly detection, reconciliation, observability, and alerting.
- Build curated, reusable, and documented data products for downstream teams.
- Design event-driven and near-real-time data solutions for transaction monitoring, risk and fraud signals, and operational intelligence.
- Improve data platform scalability, performance, reliability, and cost efficiency across compute, storage, and pipeline workloads.
- Apply engineering practices including modular design, automated testing, CI/CD, observability, lineage, documentation, infrastructure as code, and operational ownership.
- Partner with Data Science and Machine Learning teams to deliver datasets and pipelines for model development, experimentation, and production workloads.
- Lead technical initiatives, contribute to architecture decisions, mentor engineers, and partner with stakeholders to translate business problems into scalable data solutions.
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